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The Semantic Translation Bridge
作者
MilesXiang
· GitHub ↗
· v1.1.0
· MIT-0
154
总下载
0
收藏
0
当前安装
2
版本数
在 OpenClaw 中安装
/install s2-classic-scene-parser
功能描述
Translates rigid scene triggers into rich 6-element spatial intents for smart home control with manual override and personalized mode roaming.
安全使用建议
This package is largely a local simulator that creates and reads files in whatever directory you run it from. Before running: 1) review skill.py yourself (it is short and readable); 2) run it in an isolated/empty directory or in a container/VM to avoid accidentally creating or overwriting s2_* files or s2_chronos.db in important locations (S2_ROOT = os.getcwd()); 3) note that the 'roaming' and 'permission stripping' claims are narrative: the code only sets local variables and prints status, it does not contact external services or change real hotel systems; 4) if you plan to integrate this with a real S2 ecosystem or networked agents, perform a security review for network calls or auth handling at that integration boundary; 5) do not run as root/admin unless you understand the implications.
功能分析
Type: OpenClaw Skill
Name: s2-classic-scene-parser
Version: 1.1.0
The skill bundle implements a semantic parser for smart home scenes within a fictional 'S2' ecosystem. The Python code (skill.py) performs local file operations and manages a SQLite database to simulate 'Avatar Roaming' and scene logging. It uses only standard libraries, lacks any network activity or suspicious execution patterns, and its instructions in SKILL.md are consistent with the provided simulation logic without attempting to subvert the agent's behavior.
能力评估
Purpose & Capability
Name/description (semantic bridge / avatar roaming) match what the package actually does: translate scene names into textual '6-element' intents, merge local avatar habits, write timeline tracks, and log mandates to a local SQLite DB. No unrelated credentials or binaries are requested.
Instruction Scope
SKILL.md instructs the user to run python skill.py and explains the simulated IPC steps. The instructions and code are interactive and will create/read/write files (avatar_habits.json, house_topology.json, rendered_tracks.json, s2_chronos.db) in the current working directory. The README language is grandiose (claims to 'strip hotel AI agent permissions'), but the implementation only simulates that behavior locally—there is no external network/permission escalation in the code.
Install Mechanism
No install spec; this is instruction + code only. No external downloads, package installs, or archive extraction are performed by the skill.
Credentials
The skill requests no environment variables or credentials. All state is stored in local files under the current working directory; no secrets are requested or used.
Persistence & Privilege
always:false and no autonomous elevation. The skill persists data by creating directories, JSON files, and an SQLite DB in os.getcwd(); this is expected for a simulator but means running it in an important filesystem location could overwrite or add files. It does not modify other skills or system-wide settings.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install s2-classic-scene-parser - 安装完成后，直接呼叫该 Skill 的名称或使用
/s2-classic-scene-parser触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.1.0
**Major update: S2-Classic-Scene-Parser elevated from a translation script to the central OS message bus.**
- Now functions as the core communication and orchestration bus for all S2 OS modules, routing scene triggers through Avatar memory, physical topology, 4D timeline orchestration, and causality database logging.
- Expanded documentation with a detailed 4-step inter-process communication (IPC) cascade and a comprehensive breakdown of bus orchestration across phases.
- Enhanced "Avatar Roaming" feature allows cloud-based personal habits to override local settings, enabling your preferences to take effect across different locations and hardware vendors.
- The 20-classic-scenes semantic matrix is further expanded, precisely detailing each translation from rigid scene modes to 6-element intents.
- Introduced a self-healing developer experience: auto-generates mock data, topologies, and databases if previous S2 phases are not present, ensuring a seamless demonstration and testing environment.
- Focus shifted from just semantic parsing to demonstrating industry-leading, invisible, and user-personalized smart control across multi-agent environments.
v1.0.0
s2-classic-scene-parser v1.0.0
- Initial release of the scene semantic translation engine for S2 Spatial-Primitive OS.
- Translates classic rigid scene triggers into 6-element spatial intents for improved flexibility.
- Natively supports 20+ industry-standard smart home and hospitality scenes with bilingual (English/中文) documentation.
- Provides guidance for users, developers, and manufacturers to enable semantic scene adaptation and hardware decoupling.
- Recommends modern practices for wall panel integration via universal JSON payloads, ensuring future-proof scene control.
元数据
常见问题
The Semantic Translation Bridge 是什么？
Translates rigid scene triggers into rich 6-element spatial intents for smart home control with manual override and personalized mode roaming. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 154 次。
如何安装 The Semantic Translation Bridge？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install s2-classic-scene-parser」即可一键安装，无需额外配置。
The Semantic Translation Bridge 是免费的吗？
是的，The Semantic Translation Bridge 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
The Semantic Translation Bridge 支持哪些平台？
The Semantic Translation Bridge 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 The Semantic Translation Bridge？
由 MilesXiang（@spacesq）开发并维护，当前版本 v1.1.0。
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